Spillover Index Research Articles

Connectedness mechanisms in the “Carbon-Commodity-Finance” system: Investment and management policy implications for emerging economies

Prior studies investigating the interactions between the carbon market with other markets are confined to developed countries, largely overlooking emerging economies that encounter environmental dilemmas caused by high carbon emission coupling with an economic boom. This study examines the connectedness mechanisms in the “Carbon-Commodity-Finance” system in emerging economies by applying a spillover index approach with the estimated vector autoregression model. Given the recent green development for tackling climate change, this study also looks into the role of green bonds and new energy index stocks in the system. Our results suggest that: (i) the pattern of system-wide spillovers changes over time and is notably driven by economic policy uncertainties; (ii) the stock market is the system's primary source of shock contagion, with green bonds being the largest shock receiver; (iii) the carbon market is heterogeneously connected with commodity and financial markets, receiving shocks from stock, silver and copper markets, and transmitting the shock to the gold market, while interacting across carbon markets directly or indirectly through energy markets, foreign exchange rates, and green bonds; and (iv) investment risk in most markets in the system can be greatly reduced by creating portfolios with other markets, except for green bonds, which are not ideal as hedging tools. These findings have significant implications for investors and policy makers in emerging economies to plan actions for asset allocation optimization and market risk management.

The impact of the COVID-19 pandemic on the global dynamic spillover of financial market risk.

The COVID-19 outbreak has greatly impacted the stability of the global financial markets. In the post-COVID-19 pandemic era, the risk contagion patterns of the global financial markets may change. This paper utilizes the conditional value-at-risk (ΔCoVaR) model to measure the risk level of the financial markets in various economies and uses the TVP-VAR-CONNECTEDNESS approach to construct a time-varying spillover index. Based on the dimensions of time and space, we explored the contagion path, contagion status, and contagion structure characteristics of global financial market risk before and during the COVID-19 pandemic. The results entail several conclusions. (i) The COVID-19 pandemic increased the spillover level of global financial market risk and the risk connectedness of financial markets in different countries. In addition, during the concentrated outbreak period of COVID-19, the risk spillover level in developing countries rose rapidly, while the financial risk spillover level in developed countries decreased significantly. (ii) The impact of the COVID-19 pandemic on the spillover of the global financial market risk is time-varying, and there is a strong correlation between the risk spillover level of the financial markets of the world and the severity of the COVID-19 pandemic. (iii) Due to the impact of the COVID-19 pandemic, Brazil, Canada, and Russia have become new risk spillover centers; in the post-COVID-19 pandemic era, China's spillover to developed countries has increased, and the financial influence of China has also gradually increased. In addition, the risk contagion capacity of financial markets among European countries is gradually converging. (iv) During the concentrated outbreak of the COVID-19 pandemic, the Americas were the main exporter of global financial market risk, while Europe played a role in risk absorption.

Green bonds and conventional financial markets in China: A tale of three transmission modes

Existing research does not provide enough knowledge support for Chinese investors to adjust their investment portfolio to integrate green bonds. Given this background, this paper identifies how green bonds transmit information with other assets from three aspects return, volatility, and tail risks. We apply the time-frequency generalized spillover index method and the MVMQ-CAViaR model to shed light on this issue. Our empirical evidence shows that green bonds and fixed-income assets have strong pairwise information transmission in both the return and volatility spillovers, while the equity assets produce the most extreme risk spillovers. The green-connected assets (Green investment stocks and energy commodities) always display weak spillover effects, revealing that few investors have a green preference in the Chinese green bonds market. Moreover, we find that the green bonds would have a different net spillover role across frequency scales and that there are some asymmetric characteristics in extreme risk contagion, which also supplements the understanding of the information transmission modes. In short, this paper provides a comprehensive view of the cross-market information dependencies of Chinese green bonds, thereby obtaining some instructive implications for both green bond investors and policymakers.

Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switchbetween positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.

Spillovers and contagion between BRIC and G7 markets: New evidence from time-frequency analysis.

We examine the time-frequency spillovers, contagion, and pairwise interrelations between the BRIC index and its constituents, and between BRIC and G7 economies. The extent of interdependencies between market blocs and their constituents needs to be ascertained in the time-frequency domain for efficient asset allocation and portfolio management. Accordingly, the Baruník and Křehlík spillover index is employed with daily data between 11th December 2015 and 28th May 2021. We find the overall and net spillovers between BRIC and G7 to be significant in the short-term, with France, Germany, and the UK transmitting the greatest shocks to BRIC markets. We find no significant evidence of any sporadic volatilities for the studied markets in the COVID-19 period across all frequencies. However, we reveal contagious spillovers between the BRIC and G7 economies across all time scales in 2017 and 2019, which respectively reflect the persistent effect of Brexit and the US-China trade tension. Our findings divulge that in the short-term (mid-to-long-term), France and the UK (Canada and the US), are the sources of contagion between the BRIC and G7 markets. From the net-pairwise spillovers, we report high connectedness between the BRIC index and its members. BRIC countries are found to be transmitters of net-pairwise spillovers to the G7 markets excluding Japan. We recommend portfolio diversification using BRIC and G7 stocks in the intermediate-to-long-term horizon, where spillovers are less concentrated. Additionally, since individual markets are impacted by their unique shocks, investors should pay close attention to these shocks when distributing assets. In the interim, policy-makers and governments across the globe should ensure effective liberalisation of their economies to encourage international trade flows to boost portfolio diversification.

When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic

This paper examines the dynamic spillovers among the major cryptocurrencies under different market conditions and accounts for the ongoing COVID-19 health crisis. We also investigate whether cryptocurrency policy (CCPO) uncertainty and cryptocurrency price (CCPR) uncertainty affect the dynamic connectedness. We adopt the Quantile-VAR approach to capture the left and right tails of the distributions corresponding to return spillovers under different market conditions. Generally, cryptocurrencies show heterogeneous responses to the occurrence of the COVID-19 pandemic. We find that the total spillover index (TCI) varies across quantiles and rises widely during extreme market conditions, with a noticeable impact of the COVID-19 pandemic. Bitcoin lost its position as a dominant “hedger” during the health crisis, while Litecoin became the most dominant “hedger” and/or “safe-haven” asset before and during the pandemic period. Moreover, our analysis shows a significant impact of market uncertainties on total and net connectedness among the five cryptocurrencies. We argue that the COVID-19 pandemic crisis plays a vital role on the relationship between CCPO as well as CCPR and the dynamic connectedness across all market conditions.

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.

Spillover Network among Economic Sentiment and Economic Policy Uncertainty in Europe

We explore the directional spillover network among economic sentiment indicators and the economic policy uncertainty (EPU) index from Europe. We derive our results by fitting the directional spillover index approach to the monthly frequency data of eleven European countries, economic sentiment indicators and the European EPU index, spanning from 1 January 1987, to 1 February 2019. The empirical results indicate that the economic sentiment indicators of the largest European economies (Germany, France, and Italy) spillover with each other the most. The economic sentiment indicators of Germany and France most strongly influence the EU and Euro area economic sentiment indicators. The economic sentiment indicators of France and Italy have the most influence on the European EPU index, while the latter has the strongest influence on the economic sentiment indicators of Germany and France.

Market integration in the Australian National Electricity Market: Fresh evidence from asymmetric time-frequency connectedness

Australian National Electricity Market (NEM) provides efficient and smooth electricity transmission via a unique integration mechanism of different Australian regional electricity markets. We, therefore, examined the asymmetric time-frequency connectedness across five physically interconnected Australian regional electricity markets in the NEM using daily wholesale price data from 17 May 2005 to 31 December 2020. Due to direct physical interconnections and close geographical vicinities, Australian regional electricity markets have more connectedness within the region than across regions. The results of deseasonalized data revealed high spillovers among electricity markets during a crisis event, periods of abnormal weather, and regulatory sanctions implied on the NEM, which the findings from previous studies have not fully captured. Meanwhile, seasonality-adjusted results using X 13-ARIMA method highlighted dominance of negative spillovers both in the short- and long-run. Moreover, reforms in electricity policies by the Australian government played a significant role in shaping the total spillover index. Our study offers fresh insights on the NEM and stipulates significant policy implications for NEM, which the Australian state and federal government, policymakers, investors, retailers, and power suppliers must consider.

Energy security versus food security: An analysis of fuel ethanol- related markets using the spillover index and partial wavelet coherence approaches

The recent surge in biofuel production has caused concern about energy and food security. Most of the literature related to the “fuel vs. food” issue has paid close attention to futures markets, in which commodity price volatilities are greatly increased by the many non-commercial traders that are responsible for differences in price movements between financial and non-financial markets, such as retail, wholesale, and farm-gate markets. This analysis focused on the wholesale and producer prices of fuel ethanol, gasoline, and corn to analyze the interrelations among the three commodity markets from January 2001 to December 2020. We applied the spillover index and partial wavelet coherence methods, which have rarely been used in this research area, and obtained results that were largely consistent. Our primary findings are as follows: First, gasoline and ethanol price returns tend to be positively correlated with the corn price return in the short term. Second, the price return of gasoline is positively related to the corn price return in the medium and long terms, while the price return of ethanol is negatively related to the corn price return in the medium term. Finally, there is evidence that the price returns of gasoline positively lead the price return in all the frequency domains. The results suggest that while biofuel policy in the US enhances the country's energy independence, the strategy adversely affects the access, stability, and availability of the country's food security. The repeal of the US Energy Policy Act and Energy Independence and Security Act would bring about a double dividend: higher efficiency of resource allocation and improved food security.

Dynamic spillover transmission in agricultural commodity markets: What has changed after the COVID-19 threat?

This paper applies the dynamic Diebold–Yilmaz and Baruník–Křehlík spillover indices to document a closer integration between agricultural commodity markets in the period when markets rebounded after the COVID-19 threat to the Russia–Ukraine​ war. We also identified the record return spillover transmission among agricultural commodities during a time of conflict and the strongest transmitters are wheat, maize and barley. The findings emphasise an increasing uncertainty to the global food market.

Asymmetric dynamic spillover effect between cryptocurrency and China's financial market: Evidence from TVP-VAR based connectedness approach

In this paper, we constructed a volatility spillover index based on the time-varying parameter vector autoregressions (TVP-VAR) model to study the asymmetric volatility spillover effect between cryptocurrency and China's financial market. Our results show that the impact of cryptocurrency on China's financial market is relatively strong, but the impact of China's financial market on cryptocurrency is very weak. Furthermore, negative spillovers are stronger than positive spillovers. The average negative volatility spillover is dominant for Bitcoin and Ethereum, but the average positive volatility spillover is dominant for Ripple. This study has implications for investors and policymakers.

Geopolitical risk and the systemic risk in the commodity markets under the war in Ukraine

We evaluate the transmission of returns and volatility in the universe of commodities around the war in Ukraine. The total volatility spillover increases from 35% to 85%, exceeding the level seen during the pandemic. The role of commodities changes in both return and volatility spillover systems. Crude oil becomes a net transmitter of return spillovers whereas wheat and soybeans become net receivers of return spillovers. Silver, gold, copper, platinum, aluminium, and sugar become net transmitters of volatility. Geopolitical risk Granger causes the spillover indices. High levels of return and volatility spillovers are associated with high levels of geopolitical risk.

Volatility Spillover Effects in the Moroccan Interbank Sector before and during the COVID-19 Crisis

The objective of this paper is to analyze the volatility spillover effects in the Moroccan interbank sector before and during the COVID-19 pandemic crisis using the DY model. Specifically, this study assesses the impact of the recent COVID-19 outbreak on the transmission of volatility among Moroccan banks listed in the Moroccan stock market. The data sample frequency is daily and extends from 1 January 2012 to 31 December 2021, excluding holidays. The empirical results indicate that the volatility spillover index increased during the pandemic crisis. We also found varying degrees of interdependence and spillover effects between the six publicly traded Moroccan banks and the Moroccan banking sector stock index before and during the COVID-19 pandemic crisis.

Exploring the dynamics of the equity–commodity nexus: A study of base metal futures

AbstractThis empirical exercise explores different aspects of the time‐varying linkage between the commodity and equity markets in India, focusing on base metal futures. The Dynamic Conditional Correlation model (2002) and Diebold–Yilmaz spillover index (2012) are employed to ascertain the presence, pattern, direction, and magnitude of the connectedness between the returns of base metal futures and related equity indices over the period of 2006–2019. The study builds on a less‐studied “input” channel of linkage between the two, reckoning with the economic fundamentals of demand–supply interaction. Our results show that 36% of the return forecast error variance originates from spillovers, to which the contribution of equity indices is minimal. It indicates segmentation between the two markets which offer the scope for potential diversification. Further, base metal futures are the net transmitters and equity indices are the net recipients of spillovers. Positive correlations outweigh the negative correlations, in both frequency and magnitude.

Nonlinear Dependence and Spillovers between Currency Markets and Global Economic Variables

The widespread integration and growing systemic dependence among currency, stock, and commodity markets render these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we aim to explore the spillovers and nonlinear dependencies between the seven major foreign exchange rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by employing a directional spillover network approach. We also use a multi-scale decomposition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. The major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the Euro–Dollar exchange rate as the largest contributor of connectedness to the other series.

PORTFOLIO VOLATILITY SPILLOVER

In this paper, the authors estimate portfolio volatilities and use variance−decomposition techniques and Cholesky factorization to construct a portfolio volatility spillover index. Furthermore, the authors show that spillover risks are persistent and much more common than well-known indicators like the turbulence index and the CBOE VIX index might suggest. Moreover, portfolio volatilities show contributions to and from other portfolio volatilities, which indicate elevated financial market interconnectedness and heightened risk. Foreign exchange contributes strong volatility shocks to other portfolios. More importantly, its contribution has a different magnitude and direction after the Global Financial Crisis (GFC) than before depending on the base currency. To show the causality effects of portfolio volatility spillover the authors apply both econometric models and graph theory, demonstrating that the importance is time varying. This requires monitoring and analysis in decision making regarding portfolio management, portfolio construction, and risk management.

The Dynamic Spillover between Renewable Energy, Crude Oil and Carbon Market: New Evidence from Time and Frequency Domains

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it’s mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers.

Systemic spillover dynamics of crude oil with Indian Financial indicators in post WPI revision and COVID era

The study first investigates the sensitivity of major Indian financial indicators, viz. Equity Index (Nifty), Exchange Rate (USDINR), Bond (Govt 10Y Bond), Gold, Agricultural Index (N-Krishi) to ascertain the existence of significant sensitivity to standard shocks Engle and Campos-Martins (2020) either arising due to global political/economic factor or endogenous macroeconomic scenario. After that, the study undertakes the systemic spillover dynamics of Crude by estimating a self-exciting regime-switching Threshold Vector Auto-regression model based on cut-off values of Crude to test shock transmission from Crude amid major global events empirically. The findings indicate the existence of three regimes with threshold cut-offs for Crude return, viz. −2.358% and 2.294% for 2008 and 2020, respectively. After that Spillover Index is estimated across the three regimes. The findings indicate a sporadic increase in spillover during the Covid era, GFC and Oil Crisis. Despite Govt. Bond ranking high insensitivity, the spillover linkage is low. The results also indicate that, on average, systemic volatility shocks from crude oil are highest in Gold. In fact, Gold's sensitivity to crude price fluctuations escalates to new heights during the 2008–09 crisis, thus serving as a source of idiosyncratic shocks. Moreover, in recent duration, a unique see-saw kind of link has emerged between crude oil and Gold, where downward pressure by Crude on USDINR is eased by a fall in gold imports. Moreover, an analysis of the spillover pattern across the Wholesale Price Index (WPI) 2012 shows an intensification of spillover from Crude. The study is beneficial to policymakers to apply a systemic approach while empirically analyzing the spillover from a Global commodity such as Crude.

Spillover effects and nonlinear correlations between carbon emissions and stock markets: An empirical analysis of China's carbon-intensive industries

This study analyzes the correlations between carbon emissions and related industry stock markets in China. We propose a carbon-intensive industry index firstly. According to the Spillover Index, the two markets interact and the carbon-intensive stock market acts as an information receiver in most cases. We apply the Multifractal Cross-Correlation Analysis, Detrended Cross-Correlation Coefficient, and the modified Time-lagged Detrended Cross-Correlation to characterize the nonlinear correlations between carbon allowance price returns and the stock returns of carbon-intensive industries. The carbon emission trading market is found to be more volatile and inefficient than the stock market; the properties of the carbon trading pilot explain the direction and magnitude of the nonlinear correlations. The level of intervention by the government authority affects the magnitude of correlations; the strongest dynamic return spillovers and the most persistent time-lag cross-correlations were observed in the Shenzhen pilot—the most market-oriented carbon emission trading pilot in China. The allowance allocation method plays an important role in deciding the direction of the nonlinear correlations. Our empirical results show the stock returns of carbon-intensive industries and the carbon allowance price returns were positively cross-correlated in Shenzhen and Shanghai pilots, while the cross-correlations were negative in Beijing, Guangdong, and Hubei pilots.